2021/12/17 by Shufan Li, Congxi Lu, Li, Shufan +9
Computer Science · Engineering · Neuroscience · #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Hearing Loss and Rehabilitation #Music and Audio Processing #Sound (cs.SD) #cs.CV #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2112.09357
12pages,12 figures. The code for this project is available at https://github.com/jacklishufan/MAIN2021
arxiv created 2021/12/17 · openalex publication_date 2021/12/17 · arxiv updated 2021/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Audiograms are a particular type of line charts representing individuals' hearing level at various frequencies. They are used by audiologists to diagnose hearing loss, and further select and tune appropriate hearing aids for customers. There have been several projects such as Autoaudio that aim to accelerate this process through means of machine learning. But all existing models at their best can only detect audiograms in images and classify them into general categories. They are unable to extract hearing level information from detected audiograms by interpreting the marks, axis, and lines. To address this issue, we propose a Multi-stage Audiogram Interpretation Network (MAIN) that directly reads hearing level data from photos of audiograms. We also established Open Audiogram, an open dataset of audiogram images with annotations of marks and axes on which we trained and evaluated our proposed model. Experiments show that our model is feasible and reliable.